Characterization of the corrosion of iron using a smartphone camera

Abstract

Smartphone technology provides bountiful opportunities for greater participation in scientific and technological research. Digital camera image sensors have been used for the detection, measurement, and monitoring of corrosion; this work extends that capability to the smartphone. It has been observed that as the corrosion increased in clean iron, red responses decreased proportionally. Green and blue responses quantifiably decreased faster, matching the observed overall reddening as the corrosion increased. Potential noise sources due to the variable texture of the corroded samples had a negligible effect on the results. The effectiveness of this method for the characterization of a smartphone image sensor response to the degree of iron corrosion was reflected in congruent validation tests and errors less than 5%. These results demonstrate that the smartphone may be employed as a low cost and efficient means for the evaluation of surface corrosion.

Recent approaches for optical smartphone sensing in resource-limited settings: a brief review

“…Smartphone sensing has been most commonly demonstrated with colorimetric assays for a wide variety of targets using both novel and well-characterized techniques (Table 1). Recent smartphone-integrated colorimetric assays have been demonstrated for organic molecules (furfural, 32 toxins, 33 hormones, 34,35 formaldehyde, 36 carbohydrates [37][38][39][40][41] ), ions and inorganic molecules (pH, 28,42,43 uoride, 44 chlorine derivatives 27 ), heavy metal targets (Cr species, 45 Fe, 46 Pb and Cu species, 47 Hg 48 ), biological targets (nucleic acids, 25,49 enzyme activity, 50 antigens, 51,52 allergens 53 ), dissolved gases, 54,55 as well as radiometric and photometric interactions (ultraviolet radiation, 56 soil hue 57 ). These have been detected primarily from liquid media, ranging from buffer solutions to more complex and direct sampling matrices, including blood, sweat, and beverages.…”

Karakter Morfologi Talas (Colocasia Esculenta) Sebagai Indikator Level Kadar Oksalat Menggunakan Lensa Makro

“…Smartphone yang dilengkapi dengan lensa makro, merupakan perangkat yang sudah akrab bagi masyarakat pada saat ini dan telah banyak digunakan sebagai alat karakterisasi pada beberapa penelitian. Sensor gambar pada kamera digital telah digunakan untuk deteksi, pengukuran, dan pemantauan pada permukaan suatu benda sehingga pekerjaan ini memperluas penggunaan sensor gambar smartphone untuk menentukan adanya suatu senyawa dan apabila dikombinasikan dengan lensa makro dapat menghasilkan perbesaran 60-100 kali dengan limit deteksi mencapai (Igoe & Parisi, 2016).…”

A sensor-centric survey on the development of smartphone measurement and sensing systems

“…Despite the increasing interest in smartphone based sensing systems, there are also some gaps that from [190]. colorimetric alcohol concentration in saliva 0 -0.3% [38] colorimetric pH, protein and glucose in urine 5 -9, 0 -100 mg/dL, 0 -300 mg/dL [39] colorimetric blood hematocrit level 10 -65% [41] colorimetric streptomycin concentration in food 50 -267 nM [42] colorimetric BSA, catalase enzyme and carbohydrate 0 -1 mg/mL, 0 -1 mg/mL, 0 -140 µg/mL [43] colorimetric cloud coverage 4 -98% [48] colorimetric surface corrosion of iron N/A [50] irradiance measurement UVA solar irradiance 0 -10 mW/m 2 [60] irradiance measurement UVA aerosol optical depth 0.05 -0.20 [61] irradiance measurement UVB solar irradiance 1 -9 mW/m 2 [63] irradiance measurement atmospheric total ozone column 260 -320 DU [65] irradiance measurement SO 2 volcanic emission 0 -3. computer vision bacterial colony counter N/A [80] computer vision bacterial colony counter 0 -250 CFU [81] computer vision bacterial colony counter N/A [82] computer vision bacterial colony counter 0 -500 CFU [83] computer vision surveillance of fruit flies N/A [84] computer vision food recognition tool N/A [85] computer vision food recognition and nutritional value N/A [86] computer vision heart rate measurement N/A [87] mobile microscopy cell imaging (brightfield and fluorescent) N/A [88] mobile microscopy image analysis of green algae in freshwater N/A [89] sound recording and analysis chronic lung diseases average error 5.1%, detection rate 80 -90% [90] sound recording and analysis chronic lung diseases average error 8.01% [91] sound recording and analysis number of coughs detection rate 92% [92] sound recording and analysis respiratory rate estimation error < 1% [93] sound recording and analysis nasal symptoms N/A [94] sound recording and analysis snoring quantification correlation > 0.9 [95] sound recording and analysis hearing threshold in noisy environment N/A …”